Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Author:

Nam Yoonhee,Koo Harim,Yang Yingxi,Shin Sang,Zhu Zhihan,Kim Donggeon,Cho Hee Jin,Mu Quanhua,Choi Seung Won,Sa Jason K.,Seo Yun Jee,Kim Yejin,Lee Kyoungmin,Oh Jeong-Woo,Kwon Yong-Jun,Park Woong-Yang,Kong Doo-Sik,Seol Ho Jun,Lee Jung-Il,Park Chul-Kee,Lee Hye Won,Yoon Yeup,Wang JiguangORCID

Abstract

AbstractBackgroundAlthough temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application.MethodsWe stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n= 29) and sensitive (n= 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy.ResultsIn vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P= 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression ofEGR4,PAPPA,LRRC3, andANXA3was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration inPTEN,EGFR, andCDKN2A/Bwas more frequent in TMZ-sensitive samples (Fisher’s exactP= 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P= 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage (http://www.wang-lab-hkust.com:3838/TMZEP).ConclusionsWe identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs.

Funder

NSFC Excellent Young Scientists Fund

Research Grants Council, University Grants Committee

Innovation and Technology Commission - Hong Kong

Samsung Medical Center

Publisher

Springer Science and Business Media LLC

Subject

Genetics (clinical),Genetics,Molecular Biology,Molecular Medicine

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